The present invention relates to the field of diagnostic medical imaging and provides a method for achieving “virtual resolution enhancement” (VRE) using coupled multilevel analyses for the purpose “zooming in” on a diagnostic image. The invention describes linking established technologies to create new functionality that displays via a user interface, variables of interest in order to provide information beyond the native resolution of a diagnostic medical image. For the purposes of this discussion, diagnostic medical imaging will also be referred to as non-invasive imaging since the term refers to collecting an image without puncturing or cutting the skin or otherwise entering the patient with a tool or device.
Medical imaging has become a vital component in the detection, diagnosis, and treatment of disease. The advantage of imaging in the clinical setting is the capability to obtain information about how the body is functioning while causing minimal disturbance to the system. Using techniques such as magnetic resonance imaging (MRI), computer aided tomography (CT or CAT scans), nuclear medicine imaging (e.g. PET scans), or ultrasound, medical science has the means to view structures, organs, and tissue within the body without altering the anatomy. If further information is necessary to make a diagnosis or to determine the optimal intervention, invasive techniques are used, that is techniques requiring puncturing or cutting the skin or otherwise entering the body of the patient. These typically involve collecting a sample of a tissue using techniques such as needle biopsy or surgery. Further imaging may be performed on tissue samples via histopathology techniques which use combinations of stains, chemicals, antibodies, or markers in combination with a variety of microscopy techniques to identify among other things cells, proteins and architecture. In addition, functional histology can be performed to assess the health and activity of the cells within the tissue sample.
The techniques beyond diagnostic imaging are required because the information necessary for the clinician to intervene exists below the resolution of the diagnostic image. This limitation exists because in order to obtain an image, energy must be transmitted to the patient. The resolution that can be obtained is constrained both by the mode of energy (e.g. x-rays, electromagnetic energy, sound waves) as well as guidelines for safe dosages of that energy mode to the patient. For example, a CT scanner uses energy in the form of x-rays to obtain images. However, taking into account the limitations of allowable dosage range for x-ray radiation to a patient, CT scans are approximately 80% of the way to the theoretical physical limit on the order of 0.5 millimeters (1 millimeter=10−3 meters). Therefore while rapid improvements are made in the rate and manner that CT images are captured, processed and presented, there remains a barrier to the resolution that can be achieved. When CT is used on tissue samples outside the body, resolutions on the order of 5 microns (1 micron=10−6 meters) are possible depending on the tissue type.
Unfortunately, the physical limitations of non-invasive imaging present a problem to the clinician. Events that govern the pathology or evolution of a disease process in a patient often occur at the cellular level or the protein level which are several orders of magnitude lower resolution than can be obtained via diagnostic imaging. Likewise the response of the body or tissue to an intervention such as placement of a cardiac stent or implantation of a prosthetic device is also mediated at the cellular and protein level. For example, when a patient is assessed for a heart attack, the physician is attempting to learn the state of health of a region of cardiac myocytes, the muscle cells within the heart that generate the force necessary for the heart to pump blood. The question regarding the state of myocytes is whether they are (1) receiving enough nutrients and oxygen to function normally, (2) receiving enough nutrients and oxygen to stay alive but not enough to participate in the normal functioning of the heart, (3) have died due to lack of nutrients and oxygen. The dimensions of an individual cardiac myocyte are approximately 25 microns in diameter by 100 microns in length (1 micron=1×10−6 meters). The current resolution of a high-resolution CT scanner has only recently achieved resolutions of approximately 1 mm (1 mm=1×10−3 meters). Therefore to increase the probability that the clinician will make the correct diagnosis, multiple tests are performed including electrocardiograms to assess the electrical activity of the heart, a variety of laboratory tests to determine levels of cardiac enzymes, stress tests potentially involving ultrasound of imaging, perfusion tests or nuclear imaging, techniques for assessing vasculature, as well as CT scans These tests are performed while the clinician is racing against the clock because once a myocyte dies the myocyte is not replaced or regenerated by the body and loss of the contracting capability of cardiac cells or death of cardiac cells may lead to death of the patient.
The prior art contains descriptions of computer models in the form of finite element methods applied to discretizations constructed from diagnostic images. These include finite element analyses of breast tissue constructed from MRI data sets (Azar, 2002), idealized left ventricle of a heart (Watanabe, 2004), an average or composite representation of the left and right ventricle from a pig heart (Smaill, et al 2004), MRI data of vascular compromise in the hearts of sheep (Walker, et al 2005). More recently, a review article by Carter (2005) discusses the application of the finite element method to image guided surgery using various type of imaging modalities. Although these references show the use of computational modeling in conjunction with diagnostic imaging, the technology is limited to providing calculation of variables at the level of the diagnostic image. Multilevel methods have wide us in the field of composites where the techniques are used to analyze the behavior of a complex material at the level where the constituent materials are mixed. Multilevel techniques have been applied to biological problems in a localized manner such as trabecular bone microstructure (Hollister, et al., 1991), cartilage microstructure (Wu and Herzog, 2002), articular cartilage (Schwart et al., 1994), and the annulus fibrosis of the vertebral disc (Yin et al., 2005), and heart (May-Newman, et al., 1998). These reports, though important for establishing the efficacy of the prior art, focus on developing descriptive parameters for the tissue in question but do not involve diagnostic imaging or boundary conditions corresponding to a physiological loading.
The present invention is based on the hypothesis that the capability to “zoom in” on a non-invasive diagnostic image would be advantageous in the clinical setting as well as in the medical research setting. The potential to display clinically relevant variables determined by multilevel computational modeling on a diagnostic image as well as on higher resolution images that potentially represent the patient's pathology may allow the clinician to leverage additional information that could prove critical in correct diagnosis and rapid treatment of a patient. Likewise in the research setting, the potential to “zoom in” on an image or to view relevant variables on models across multiple size scales and to view interactions from the cellular level to the tissue level may facilitate the design of clinical interventions and devices.